The Determinant Factors of Technology Adoption for Improving Firm’s Performance: An Empirical Research of Indonesia’s Electricity Company | Arifin | Gadjah Mada International Journal of Business 16898 32416 1 PB
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
Gadjah Mada International Journal of Business
Vol. 18, No. 3 (September-December 2016): 237-261
The Determinant Factors of Technology Adoption for
Improving Firm’s Performance:
An Empirical Research
of Indonesia’s Electricity Company
Zainal Arifin,1 Firmanzah,2 Avanti Fontana,2 and Setyo Hari Wijanto2
1)
PT PLN, Indonesia; 2) Faculty of Economics and Business, Universitas Indonesia
Abstract: This study investigates the determinant factors of technology adoption by connecting Technology Organizational Environment (TOE) with the dynamic capability factors. Using 518 respondents
representing 222 business units of Indonesia’s electricity company, the study found that only the absorptive capability has a positively significant effect on technology adoption. Practically, the study emphasizes
that without the absorptive capability for managing the resource, the core competence of a firm will not
occur and the adoption of technology will be less effective. Another finding is the absorptive capability’s
typology mapping the eight technology adoption statuses in an organization, based on three of the
determinant factors: the externalities, entrepreneurial leadership and slack resources.
Abstrak: Penelitian ini menguji berbagai faktor penentu pengadopsian teknologi dengan menghubungkan lingkungan
organisasional teknologi dengan faktor kapabilitas dinamis. Dengan menggunakan 518 responden yang mewakili 222 unit
bisnis Perusahaan Listrik Negara (PLN), studi ini menemukan bahwa hanya kapabilitas absorptif yang mempunyai
pengaruh positif signifikan pada pengadopsian teknologi. Dalam praktiknya, studi ini menekankan bahwa tanpa kapabilitas
absorptif untuk pengelolaan sumber daya, kompetensi inti perusahaan tidak akan tercipta, sehingga pengadopsian teknologi
menjadi kurang efektif. Temuan lain adalah tipologi kapabilitas absorptif memetakan 8 (delapan) status pengadopsian
teknologi dalam organisasi berdasarkan tiga faktor penentunya: eksternalitas, kepemimpinan kewirausahaan dan sumber
daya yang tertunda.
Keywords: absorptive capability; electricity utility; technology adoption; TOE framework
JEL classification: D83, L94, O33
* Corresponding author’s e-mail: zainal.arifin22@pln.co.id
ISSN: 1141-1128
http://journal.ugm.ac.id/gamaijb
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Arifin et al.
Introduction
Technology has improved and continues to improve the way we live, communicate, interact socially and do business. In the
context of a firm, Dussauge (1992) argued
that technology is a factor affecting many
aspects of a firm’s strategy. The technological
changes and innovations were fundamental
sources of productivity and sustainable
growth (Morrris 1998; Johnson et al. 2008;
Van Ark et al. 2008). Thus, viewing technology’s adoption as a consistent process is
the key to successfully adopt and use technology. Strategically, the successful adoption
of technology by firms significantly affects
their competitive advantage, especially their
performance (Porter 1985; Barney 1991;
Majundar and Ventaraman 1998; Rayport and
Jaworski 2004; Kotler and Keller 2006).
Concerning this issue, some research
studying the use of technology in production
processes to increase a firm’s productivity has
been conducted in the 19th and 20th centuries (Abramovitz 1956; Solow 1957; Saloner
and Shepard 1995). Further studies have
linked technology to firms’ performance, as
measured through wages, the firms’ productivity, growth, and other factors (Bressler et
al. 2011). Many studies argued that technology’s adoption had significantly affected
the firms’ performance (SSinha and Noble
2008; Sabbaghi and Vaidyanathan 2008;
Benitez-Amado et al. 2010; Bressler et al.
2011; Adewoje and Akanbi 2012).
However the impact of technology’s
adoption remains inconclusive. Some studies proved that IT’s (Information Technology)
adoption contributed to an up to 81 percent
increase in output (Brynjolfsson and Hitt
2000), reduced labor costs by up to 40 percent (Rodd 2004), increased efficiency and
the total productivity of the adopting firms
238
(Chandrasekhar et al. 2008; Benitez-Amado
et al. 2010), enhanced the firms’ profitability
(Adewoje and Akanbi 2012; Kabiru and
Usman 2012), and improved the firms’ financial profits (Sarker and Valacich 2010). On
the other hand some empirical studies did not
find any relevant improvements in productivity associated with investment in technology (Quinn and Baily 1994; Becchetti et al.
2006). Berndt and Morrison (1995) also found
a negative relationship between profitability
and investment in IT. Thus, the notion of the
productivity paradox of IT was created and
has been one of the main issues in IT research
areas (Raymond and Blili 1997). Shu and
Strassmann (2005) also noticed that ICT
technology cannot improve firms’ earnings
in terms of their return on assets. In addition
a quantitative research by Jawabreh et al.
(2012) found that there was a negative correlation between the adoption of technology
and the profit rate of the airlines adopting it.
This paradox requires further research
to examine what are the initial determinant
factors of technology’s adoption and what is
the mediating factor which determines the
relationship between technology’s adoption
and firm’s performance. At the firms’ level,
the TOE (Technology - Organization - Environment) model has been considered to be
the most effective tool explaining technology’s
adoption (Oliveira and Martins 2011). However it is not sufficient to analyze technology’s
adoption in relation to a firm’s performance
in dynamic circumstances. Viewing technology’s adoption as a process for managing
some resources; it could be analyzed and determined by RBV (Resource Based View). In
order to develop its competitive advantage,
a firm must have resources and capabilities
that are superior to its competitors (Barney
1991). The firm’s resources and capabilities
together form its distinctive competencies.
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
Literature Review
The study of the adoption of technology can be approached from several levels
(Taylor and Tod 1995). Some researchers investigate its adoption from a macro-view
within the social context or at the country
level (Kiiski and Pohjola 2002). Others have
examined this issue at the organizational or
intra-firm level (Plouffe et al. 2001). Some
others focused on investigating technology’s
adoption by its individual determinants
(Bagozzi et al. 1992; Davis 1989; Venkatesh
et al. 2003).
Extending Taylor and Todd’s (1995)
classification, the research into the determinants of technology’s adoption could be distinguished into three streams: Firstly, those
based on intention-based models relying on
how users accept or do not accept it, and further use or reject technology; secondly, diffusion innovation, focusing on why and how a
new technology spreads around an organization or community; and thirdly, how the new
technology affects the goals, objectives and
performance of an organization. The first
stream was exemplified by such theories as
the Technology Acceptance Model or TAM
(Davis 1989) and the Unified Theory of Acceptance and Use of Technology (UTAUT)
(Venkatesh et al. 2003). The second stream
was primarily represented by the Diffusion
Of Innovation or DOI theory (Rogers 1963;
1983; 1995), and the Technology Adoption
Life Cycle or TALC model (Rogers et al. 1957;
Moore 1991). The last one was dominantly
explained by organizational theories such as
the Technology Organization and Environment or ‘TOE’ framework (Tornatzky and
Fleischer 1990). Considering the content and
context of the research (de Wit and Meyer
2010), and exploring all those main related
theories, this paper proposed the TOE frame-
work as the most relevant theory for searching for the determinant factors of technology’s adoption at the firms’ level. The
three elements present both constraints and
opportunities for technological innovation
(Oliveira and Martins 2011).
Although RBV could explain technology’s adoption processes for achieving a firm’s
competitive advantage, it is essentially a static
theory since it does not explain how the firm’s
resources and capabilities evolve over time
to be the basis of its competitive advantage
(Priem and Butler 2001). RBV research does
not essentially examine the effects of a firm’s
external environment on how it manages its
resources. Hence there was a need for a theory
which would not just view a firm as a bundle
of resources, but also the mechanisms by
which firms’ learn and accumulate new skills
and capabilities, and the forces that limit the
ratio and direction of this process (Teece et
al. 1990). Then the concept of ‘dynamic capability’ emerged; it reflects how quickly the
capabilities and resources of the company
change following changes in an increasingly
dynamic environment (Eisenhardt and Martin 2000).
Referring to the previous Dynamic Capabilities (DC) research, the most important
relationship in this field is that of dynamic
capabilities with performance. The literature
is divided (Silva 2007); some explain that
there is a direct relationship between firms’
dynamic capabilities and their performance
or competitive advantage (Makadok 2001;
Zollo and Winter 2002). Others have linked
dynamic capabilities to competitive advantage but have asserted that this link is indirect (Zott 2003; Helfat and Peteraf 2003;
Ambrosini and Bowman 2009; Wang and
Ahmed 2007). Contrary to those ideas,
Helfat and Peterraf (2003) argued that dynamic capabilities did not necessarily lead to
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Arifin et al.
a competitive advantage. However to sustain
their competitive advantage, firms need to
renew their stock of valuable resources, as
their external environment changes because
of their dynamic capabilities’ processes
(Teece et al. 1997; Makadok 2001; Helfat
and Peterraf 2003; Wang and Ahmed 2007;
Majumdar et al. 2010).
Nowadays a firm’s absorptive capacity
is mostly conceptualized as a dynamic capability (Abreu et al. 2008). Following key empirical studies pertinent to DCs from 1996 to
2012, Dynamic capability in the form of absorptive capability has been implemented at
many levels and in the context of numerous
studies at this time. It has been widely examined at the firms’ level (Teece et al. 1997),
industries’ level (Lin and Lin 2008), intrafirm (Amlakuet al. 2012), inter-firm (Brady
and Davis 2004), SMEs (Griffith and Har vey
2001) and non-profit organizations (Zahra
and George 2002.
In addition absorptive capability is the
most applicable form of DC for many fields/
subjects. The implementation of absorptive
capability has been included in studies focusing on research and development (Caloghirou
et al. (2004), knowledge management (Corso
et al. 2006), organizational structures (Lin
2012), human resources (Caloghirou et al.
2004; Freels 2005), external interactions
(Caloghirou et al. 2004), social capital (Landry
et al. 2002), supplier integration (Malhotra
et al. 2005), client integration (Johnsen and
Ford 2006) and inter-organizational fit (Lane
and Lubatkin 1998).
These existing various relationships of
absorptive capability to firms’ performance
requires further research to examine what are
the determinant factors of absorptive capability’s effects and what is the mediating factor that connects the relationship between absorptive capability to firms’ performance.
240
Hypotheses
Following the TOE framework, there
were some relevant environmental factors
influencing firms’ adoption of technology,
such as the role of partners (Al-Qirim 2006;
Jeyaraj et al. 2006; Scupola 2009), competitive pressure (Porter and Millar 1985), and
regulatory compliance (Lai 2008, and Lin
2013). As well as their effect on the adoption of technology, those external factors also
influence how leaders’ perceive the way to
manage their organizations more efficiently
and effectively. External effects are mostly
related to the character and behavior of the
management, and how the organization interacts with its environmental dynamics; its
organizational leadership.
When the industrial environment was
more competitive, turbulent and unpredictable, it brought severe pressure to bear on
the types of analytical approaches to management. The cornerstone of competition
pushed people to think that analytical planning, which leads to competitive success, was
no longer feasible (Gupta et al. 2004). In this
chaotic and dynamic environment, where the
power of analytical leadership was diminished, the need the entrepreneurial leadership
by organizations was higher (McGrath 1997).
However the need for and emergence of entrepreneurial leadership was caused by the recent dynamic circumstance, so entrepreneurial leadership clearly was affected by external factors (McGrath and MacMillan 2000).
Additionally by considering the context of
our study, we prefer to use ‘externalities’ for
representing all the determinant factors of
technology’s adoption by an external organization; the external networks, regulations and
social issues etc.
Entrepreneurial leadership is known as
the dynamic process of presenting a vision,
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
building commitment among followers, and
risk acceptance when facing opportunities,
which causes the efficient use of the available resources, along with discovering and
utilizing new resources, with respect to the
leader’s vision (Lee and Venkataraman 2006).
The most important feature of entrepreneurial leadership is creating value, by discovering new opportunities and creating new strategies in order to gain a competitive advantage (Schulz and Hofer 1999). Moreover,
entrepreneurial leadership’s development occurs during the process of transforming the
knowledge acquired from experience and social interaction, allowing the opportunities for
personal development and business creation
to be identified (Churchill et al. 2013). Hence:
Hypothesis 1: the externalities positively affect the
entrepreneurial leadership.
On one hand, such a capability mostly
depends on technological collaborations, formal or informal networks between firms and
their external pressures, such as industries and
professional groups, and between industry
and university laboratories (Massini and
Lewin 2003). Furthermore, this relationship
would increase the effectiveness of knowledge’s absorption capability as it enhances the
complementary asset of experience inside the
firm (Cohen and Levinthal 1990). Then, collaboration and partnerships could be a learning resource for an organization, that helps
companies to recognize dysfunctional routines and to avoid hidden strategic constraints
(Teece 2007). While the study by Helfat and
Peterraf (2003) stated that all dynamic capability processes adapt, integrate and
reconfigure internal and external organizational skills, resources and functional
competences, in order to match the requirements of a changing environment. It was further emphasized by Rindova and Kotha
(2001) who explained how a dynamic market and turbulent industry pushes firms to
enter tough fields of competition through
their continuous organizational absorptive
capabilities. So:
Hypothesis 2: the externalities positively affect absorptive capability.
As well as its effect on absorptive capability, the externalities also had the effect
of slackening the resources. Bourgeois (1981)
argued that, in practical terms, slack resources
can serve some primary functions. They allow the organization to offer salaries that are
higher than those actually required to retain
the employees’ services. In addition slack resources also aid conflict resolution when
problem solving. Other functions of slack
resources are as a buffering mechanism, used
to adapt to sudden changes in the environment. Then the most important function of
slack resources is to facilitate strategic or creative behavior to help make long-term decisions such as seizing a business opportunity,
developing a new product, or realizing a
growth strategy. In summary, the functions
of slack resources are related to internal tensions within the organization, and also to external tensions between the organization and
its environment (the externalities). Then some
studies found that environmental conditions
or externalities are one of the antecedents
leading to the development of slack resources
(Stevens 2002; Donada and Dostaler 2005).
Thus:
Hypothesis 3: the externalities positively affect slack
resources.
Some recent literature has shown that
firms benefit from having absorptive capabilities when crafting new business and corporate strategies (Ambrosini and Bowman
2009); learning new skills (Zollo and Winter
2002; Ambrosini and Bowman 2009); lever241
Arifin et al.
aging their other resources (Ambrosini and
Bowman 2009); introducing innovative programs that stimulate strategic changes
(Repenning and Sterman 2002); and successfully commercializing new technologies generated through their R&D activities (Marsh
and Stock 2003). Other studies argue that absorptive capability’s effects are indirect; they
emerge through such things as entrepreneurial capability (Zahra 2006) and organizational
leadership (Hejazi et al. 2012).
Even though there are opposite views
in some researches’ findings about the importance of entrepreneurial and leadership activities for the conception, development, configuration and maintenance of the absorptive
capabilities in an organization (Repenning
and Sterman 2002; Zahra et al. 2006), some
recent studies argue that several best practice processes supporting knowledge acquisition, knowledge creation, and knowledge
integration in firms have affected their entrepreneurial leadership’s status (Simsek et al.
2010). So:
Hypothesis 4: the absorptive capability positively
affects entrepreneurial leadership.
Internally slack resources could be a
valuable resource to improve firms’ performance through managerial initiatives. Indeed,
managerial capability is necessary to find
ways to devote slack resources to productive
activities (Burkart et al. 2003). A positive relationship between slack resources and firms’
performance is more likely in firms with
higher levels of managerial dynamic capability (McKelvie and Davidsson 2009). They
found that four dynamic capabilities had positive effects stemming from their access to
particular resources, and provided empirical
support for the notion of treating the firm as
a dynamic flow of resources, as opposed to a
static stock. Along with the study, recent
242
empirical scholarship suggests that in both
the dynamic managerial capabilities (Ireland
et al. 2003) and the micro-level foundations
of routines and capabilities (Felin and Foss
2005), researchers have found that firms’
absorptive capabilities have fundamental roles
in the fir ms’ slack resources’ status
(McKelvie and Davidsson 2009). Hence:
Hypothesis 5: the absorptive capability positively
affects slack resources.
Due to investigating technology’s adoption using RBV logic, which leads to static
circumstance, firms need dynamic capabilities to renew their stocks of valuable resources, as their external environment
changes (Teece et al. 1997; Makadok 2001;
Wang and Ahmed 2007; Majumdar et al.
2010). Considering that a firm’s absorptive
capacity is mostly conceptualized as a dynamic
capability, which has been widely researched
at the level of firms, sectors, regions and nations, based on a wide consensus (Abreu et
al. 2008). It is the ability of the organization
to recognize the values of novelty in its external forms, then assimilate and apply it for
commercial purposes (Cohen and Levinthal
1990). Specifically, the absorptive capability
measures a firm’s ability to absorb, assimilate, and exploit an innovation throughout the
firm (Link et al. 2002). The higher the level
of its absorptive capability a firm demonstrates, the more it exhibits its dynamic capabilities (Zaheer and Bell 2005).
Regardless that there are different views
of dynamic capabilities’ effects on firms’ advantages, recently there have emerged studies arguing that the absorptive capability
builds and reconfigures resource positions
(Eisenhardt and Martin 2000), zero-order
capabilities (Winter 2003), operational routines (Zollo and Winter 2002) or operational
capabilities (Helfat and Peteraf 2003) and,
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
through them, affects performance. This
chain of causality is designated as an indirect link between absorptive capabilities and
performance. The indirect relationship results
from the idea that absorptive capabilities originated and defined firms’ individual resource
configurations, including their functional capability processes, which shape the firms’
competitiveness and therefore performance
(Zott 2003). Therefore this paper argues that
technology’s adoption is one of the functional
capabilities which mediate the relationship
between the absorptive capabilities and the
firm’s performance. Thus:
Hypothesis 6: the absorptive capability positively
affects technology’s adoption.
Based on strategic management practices, another significant organizational factor of technology’s adoption is organization
leadership. Fundamentally, leadership has an
influence over organizations via their strategic decision-making, determining organizational structures and managing the organizational process (Day and Lord 1988;
Nahavandi 1993). Moreover a leader with
good perceptional resources will contribute
to higher performance (Dessler 1994). Additionally Devarajan et al. (2003) found that
the success of firms in dynamic industries
depended on ‘thriving innovation activity’,
that in turn is primarily driven by ‘effective
entrepreneurial leadership’. Such leadership,
as represented by top management, plays a
very critical role in driving innovation in firms,
and in mastering its dynamics (Kuczmarski
1998; Kipp and Michael 2001). It could be
summarized that the key factor determining
successful technological adoptions under
these circumstances is the ‘effective entrepreneurial leadership’ of the organization
(Devarajan et al. 2003). So:
Hypothesis 7: the entrepreneurial leadership positively affects technology’s adoption.
TOE’s technological and organizational
context describes that technology’s adoption
depends on the pool of resources exceeding
the minimum necessary to produce a given
level of organizational output, or slack resources (Lin 2013). The raw materials’ input
to technology’s adoption process also includes tangible and intangible assets (Prakash
et al. 2008). Instead of technology, some
TOE focused studies postulated that slack
sources for technology’s adoption were financial (Franquesa and Brandyberry 2009),
knowledge (Jeyaraj et al. 2006; Sabherwal et
al. 2006; Lin 2013; Wang et al. 2013), and
employee or human capital (Wang et al. 2013;
Vanacker et al. 2013). In conclusion, such
resources have a positive effect on a firm’s
flexibility and innovation in a dynamic environment, (Damanpour 1996; Judge et al.
2001) and provide organizations with the
ability to be proactive as well as defensive in
adopting new technologies or designing new
lines of services (Lawson 2001; and Daniels
et al. 2004). Thus:
Hypothesis 8: slack resources positively affect the
adoption of technology.
Although there are still debatable results
about the effect of technology’s adoption on
firms’ performance; most literature shows
that the use of new technology during production increases a fir m’s productivity
(Abramovitz 1956; Solow 1957; Saloner and
Shepard 1995). Many recent researchers argue that technology’s adoption brings down
the operational costs (Saloner and Shepard
1995; Rodd 2004; Chandrasekhar et al. 2008;
Benitez-Amado et al. 2010), contributes to
output increases, even if they are only marginal, (Brynjolfsson and Hitt 2000; and
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Arifin et al.
Adewoje et al. 2012), improves efficiency and
effectiveness (Milne 2006; Sabbaghi and
Vaidyanathan 2008; Rusli 2012), reduces
environmental impacts by lowering energy
costs (Bressler et al.; 2011), and also leads to
significant reductions in firms’ mortality rates
(Sinha and Noble 2008). Matching those studies, we argue that technology’s adoption has
significant effects on a firm’s performance.
Hence:
ship, slack resources and technology’s adoption. While both entrepreneurial leadership
and slack resources directly affect technology’s adoption, eventually technology’s
adoption will affect firms’ performance. However the model should be tested empirically
through a quantitative approach.
Hypothesis 9: the adoption of technology positively
affects the firm’s performance
Research Objective
The Proposed Model
All nine hypotheses construct a hypothetical conceptual model as the following
figure (Figure 1) shows, explaining the relationship between the determinant factors of
technology’s adoption at the firms’ level. The
model’s logic starts from externalities as the
antecedents; the organization is driven by the
external factors. It has an effect on the three
organizational factors: Entrepreneurial leadership, absorptive capability, and slack resources. So absorptive capability is the determining factor for entrepreneurial leader-
Methods
Starting from the TOE’s view, this study
analyzes the influence of externalities and absorptive capability on technology’s adoption
for improving firms’ performance. Other determinant factors –entrepreneurial leadership and slack resources– were also investigated to assess the relationship between
TOE’s factors with the absorptive capability
at the firms’ level. The study purposes to
empirically test a conceptual model of the
indirect effects of the externalities and absorptive capability on the adoption of technology, which can be the key predictors of
firms’ performance in a dynamic environment.
Figure 1. The Conceptual Hypotheses Model
Enterpr
H1
H4
H5
H3
Slack
244
H6
H8
Techn
H9
Extern
Absorb
H2
H7
Firm
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
Respondents and Procedure
Using an online survey, this study was
conducted by collecting data from 518 managers representing 229 business units around
Indonesia belonging to the Indonesian Electricity Company (PLN). There were 222 units
(96.94%) that completed the survey correctly,
1 unit’s reply (0.4%) was considered not to
be valid, and 6 units (2.6%) did not respond
at all. PLN was chosen because it represents
the content and context of the research issues, namely: (1) A ‘RBV perspective’ company (2) a ‘technology-intensive’ organization
where 87 percent of its assets are technological things (3) a ‘comprehensive’ technology
adoption flow, which covers both ‘top-down’
and ‘bottom-up’ processes (4) it is a ‘national
company’ with 40,000 employees spread out
over all areas of the country.
The respondents’ core business unit is
for the organization of the electricity utility
company’s supply chain, for the generation,
transmission and distribution or retail/service of electricity. Considering their similarity characteristics, the working experience of
the respondents is varied, from less than 5
years, while others have 5-10 years experience and over. Meanwhile their ages are divided into three groups: Less than 30 years
old, between 30-40 years old and over 40
years old. The number of samples, which
exceeds 100 questionnaires, is an appropriate number for research that analyzes its data
using a SEM (Structural Equation Model),
especially for the overall fit measures side,
which is represented by the likelihood-ratio
chi-square statistic (Hair et al. 1998). The
SEM analysis, which uses LISREL version
8.7 software, is done with a ‘two-stage approach,’ (Wijanto 2008), with the process as
follows: (1) Analysis of the measurement
model using Confirmatory Factor Analysis
(CFA). (2) Analysis of the structural model
to analyze the relationship among all the latent variables that have been simplified. (3)
Analysis of the significance test results for
each hypothesis to determine whether the
hypothesis will be accepted or rejected.
Measurement
For measuring the externalities, 9 selected items are used, with reference to a previous study with 3 controlling variables: An
external network ( Lin and Lin 2008), regulation (Lai 2008), and social issues (Asres et
al. 2012). Meanwhile entrepreneurial leadership is measured by the construct of the entrepreneurial leadership data from the
GLOBE (Global Leadership and Organizational Behavior Effectiveness) program
(Gupta et al. 2004). It consists of 2 main dimensions: Cast enactment and transformational enactment with a total of 19 items.
Then DC is represented by ‘absorptive capability’, which is summarized from previous
DC literatures (Abreu et al. 2008). It has 4
dimensions: Knowledge acquisition, assimilation, transformation and exploitation, with
12 items. Slack resources are represented by
3 variables: technology, knowledge, and human resources (Wang et al. 2013) with 10
items. Regarding the context of this study,
slack finance is neglected. Technology’s adoption, which has 6 items, is measured through
2 dimensions: appropriateness and effectiveness (Mirvis et al. 1991; Hall and Kahn
2002). Then the firm’s performance is observed by financial and non-financial variables with 6 items (Kabiru and Usman 2012).
Overall, 62 items using 6 Likert-type scales
are used to measure the 6 latent variables.
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Arifin et al.
Result
lower than their standard (RSMEA and Std
RMR). Using LISREL the test presents the
calculated value of RSMEA = 0.044, NFI =
0.97, NNFI = 0.98, CFI = 0.99, IFI = 0.99,
RFI = 0.96, Std RMR = 0.045, GFI = 0.91
and AGFI = 0.87. Table 2 shows that there
is only one GOFI indicator that shows a marginal fit (AGFI), therefore it can be concluded that the overall fit of the structural
model is good.
In addition CFA also measures the validity for all the indicators (observed variables) and the reliability of the measurement
for each latent variable (construct). As presented in Table 3, from the 19 observed variables of the model, there are 2 indicators of
the externalities construct (regulation and
social issues) which have a SLF (Standardized Loading Factor) of < 0.70 (not good
validity). The table also shows that 2 of the
6 latent variables have a CR (Construct Reliability) score of < 0.70 and a VE (Variance
Extracted) score of < 0.50. This means that
the latent variables of the externalities and
slack resources are less reliable. However the
CFA test confirmed that the overall variables
of the measurement model have good reliability and validity.
The loading factors for all the items can
be seen in Appendix 1, and the result of the
descriptive statistical analysis can be seen in
Table 1. Even though the standard deviation
is included in the analysis, the latent variables’
score is still higher than average. For example,
slack resources, with a standard deviation of
0.53, has a lowest limit of 2.66 (3.19 – 0.53).
This value shows that most of the respondents believe that their actual perceptions are
similar. Additionally based on the ANOVA
test result, in general there are no differences
for all the latent variables in the respondent’s
profile group which refer to those discriminate factors: Type, location and the size of
the organization’s respondents.
Then a Confirmatory Factor Analysis
(CFA) is used to test how well the measured
variables represent the number of constructs.
It is conducted to specify the number of factors required in the data, and which measured
variable is related to which latent variable. To
have a good overall fit of the measurement
model, some of the GOFI (Goodness of Fit
Index) indicators should be higher than the
standard values (NFI, NNFI, CFI, IFI, RFI,
GFI and AGFI) and two others should be
Table 1. Descriptive Statistics Analysis Result
246
Construct
Number
of items
Mean
Standard
Deviation
Skewness
Kurtosis
Externalities
Entrepreneurial leadership
Absorptive capability
Slack resources
Technology adoption
Firm performance
9
19
12
10
6
6
4.77
4.96
4.53
2.81
4.76
4.14
0.54
0.59
0.57
0.53
0.59
0.68
-0.67
-0.94
-0.43
-0.36
-0.94
-0.75
1.20
1.66
1.05
0.83
2.90
1.57
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
Table 2. GOFI Values of the Structural Model Test
GOFI
RSMEA
NFI
NNFI
CFI
IFI
RFI
Std. RMR
GFI
AGFI
Calculated Values
Standard Value
0.04
0.97
0.99
0.99
0.99
0.96
0.048
0.94
0.87
<
>
>
>
>
>
<
>
>
Conclusion
Fit is good
Fit is good
Fit is good
Fit is good
Fit is good
Fit is good
Fit is good
Fit is good
Fit is marginal
0.08
0.90
0.90
0.90
0.90
0.90
0.05
0.90
0.90
Table 3. Validity and Reliability of Measurement CFA Model
Variables/
Dimensions
*SLF ≥0.5
Absorptive capability
AcquL
0.86
AssiL
0.94
TranL
0.90
ExploL
0.78
Adoption Technology
ApproL
1.00
EffectL
0.81
Entrepreneurial leadership
BuildL
0.84
DefL
0.85
ChalL
0.94
AbsorL
1.00
UnderL
0.92
Externalities
NetL
0.78
RegL
0.49
SocL
0.45
Firm performance
FinL
0.70
NonfinL
0.92
Resource slack
TechL
0.62
KnowL
0.96
HumL
0.94
Error
*CR ≥0.7 *VE ≥0.5
0.27
0.12
0.20
0.39
0.01
0.35
0.29
0.28
0.11
0.01
0.15
0.39
0.75
0.80
0.52
0.14
0.63
0.10
0.19
0.76
0.77
0.84
0.90
0.94
0.81
0.54
0.65
0.83
0.51
0.49
0.49
*)
SLF = Standardized Loading Factor; where a good SLF score is > 0.50
**)
CR = Construct Reliability; where a good CR score is > 0.70
***)
VE = Variance Extracted; where a good VE score is > 0.50
Conclusion
Good reliability
Good validity
Good validity
Good validity
Good validity
Good reliability
Good validity
Good validity
Good reliability
Good validity
Good validity
Good validity
Good validity
Good validity
Less reliable
Good validity
Marginal validity
Marginal validity
Good reliability
Good validity
Good validity
Less reliable
Good validity
Good validity
Good validity
247
Arifin et al.
Table 4. Test Results of the Structural Research Model
Hypotheses
H1
Latent Variable’s
Relationship
Externalities
Entrepreneurial
leadership
Calculated
t-value
Structural
Coefficient
0.23
0.04
There is an insignificant
positive effect, hypothesis 1 is
rejected.
H2
Externalities
Absorptive capability
10.61
0.85
There is a significant positive
effect, hypothesis 2 is accepted.
H3
Externalities Slack
resources
0.03
0.01
H4
Absorptive capability
Entrepreneurial
leadership
There is an insignificant
positive effect, hypothesis 3 is
rejected.
3.62
0.65
There is a significant
positive effect, hypothesis 4
is accepted.
H5
Absorptive capability
Slack resources
2.66
0.53
H6
Absorptive capability
Technology adoption
There is a significant
positive effect, hypothesis 5 is
accepted
5.58
0.68
There is a significant
positive effect, hypothesis 6 is
accepted
H7
Entrepreneurial leadership
Technology adoption
-0.81
-0.07
There is an insignificant
negative effect, hypothesis 7 is
rejected
H8
Slack resources
Technology adoption
-0.62
-0.05
H9
Technology adoption
Firm performance
There is an insignificant
negative effect, hypothesis 8 is
rejected
5.23
0.84
There is a significant
positive effect, hypothesis 9 is
accepted
The calculated t-values and structural
coefficients of each latent variable, and the
hypotheses results are presented in Table 4.
From the hypotheses testing results, five hypotheses are accepted (t-value > 1.96) and four
hypotheses are rejected (t-value < 1.96). It is
surprising, finding that the externalities have
no significant effect on both entrepreneurial
leadership and slack resources (H1, H3). In
conclusion the model demonstrates that the
determinant factors of technology’s adoption
248
Conclusion
for improving a firm’s performance works
only through 1 pathway: Externalities –absorptive capability– technology adoption
(H2, H6). Even though the absorptive capability has positive significant effects on both
entrepreneurial leadership and slack resources
(H4, H5), the relationship does not significantly affect the adoption of technology
(both H7 and H8 are rejected). The study has
empirically proven that the effect of dynamic
capability –in this study represented by ab-
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
sorptive capability– on firms’ performance is
indirect mediated by technology’s adoption
(H6, H9).
Discussion
Starting from the TOE’s framework, this
study is focusing on the determinant factors
of technology’s adoption at the firms’ level,
and especially the role of externalities as the
primary antecedent and absorptive capability as a dynamic factor. Through an empirical
conceptual model, this study has found and
tested the cor relation of technology
adoption’s antecedents –in the form of the
TOE’s factors– (H1, H3), and then connected
them to a firm’s DCs (H2, H4, H5) and eventually to a firm’s performance through
technology’s adoption (H6, H7, H8, H9).
Many studies using the TOE framework have
proven that the determinants’ factors have a
significant relationship with technology’s
adoption for enhancing firms’ performance
(Al-Qirim 2006; Jeyaraj et al. 2006; Scupola
2009; Lai 2008; Lin 2013).
However, this study empirically proved
that entrepreneurial leadership has an insignificant negative relationship with technology’s adoption (H7); as well as slack resources (H8). Based on further investigation
most of the process of technology’s adoption by PLN’s business units is ‘bottom-up;’
it is likely driven by the organization rather
than the management. This is why entrepreneurial leadership has a negative insignificant
effect on technology’s adoption (Greenberger
and Sexton 1988; Roomi and Harrison 2011).
The slack resources also have a negative insignificant effect, due to some important resources such as financial and human capital,
which are not controllable by the business
units’ managers (Chau and Hui 2001 and
Franquesa and Brandyberry 2009). Those re-
sources are mostly managed by PLN’s regional
offices so that resources are ‘given’ to the
business units (Vanacker et al. 2013).
In addition, previous literature shows
that the relationship between TOE’s factors
theoretically is significantly positive, such as
the externalities to entrepreneurial leadership
(McGrath and MacMillan 2000; Cohen and
Levinthal 1990) and externalities to slack resources (Pfeffer and Salancik 1978; Sharfman
et al. 1988; Stevens 2002; Donada and
Dostaler 2005). However this study shows
that the externalities affect both entrepreneurial leadership and slack resources positively,
but not significantly (H1, H3). The insignificant effect of the externalities on entrepreneurial leadership is caused by the relationship between PLN’s business units and their
networks, such as government agencies, the
industry and their professional associations,
none of which are relevant to the development of entrepreneurial and leadership capabilities in the organization. A similar reason is also applicable for the positive, insignificant effect of the externalities on the slack
resources in PLN’s business units (Iacovau
et al. 1995, and Lin 2013).
With the recent turbulent and unpredictable circumstances there is a need for directly
connecting the TOE’s factors to the absorptive capability, especially to show the adoption of technology as a functional competence/capability in a dynamic business environment (H2, H4, H5). Moreover the study
empirically proves that the effect of absorptive capability –as a representative of DC on the firm’s performance is indirect and - in
this research– mediated by technology’s adoption (H6, H9). This supports some previous
studies (Zott 2003; Helfat and Peteraf 2003;
Ambrosini and Bowman 2009; Wang and
Ahmed 2007). Practically, the adoption of
technology by PLN’s business units is mostly
249
Arifin et al.
an output of such absorptive capability, such
as knowledge management practices.
Technology’s adoption is commercial ‘knowledge implementation;’ it is one of the stages
of the knowledge management cycle in an
organization. Therefore like other intangible
resources; its effect on the organization’s performance takes a relatively long time to be
felt, as it is not direct.
This study emphasizes that, without absorptive capability –at next higher order– for
managing the resource, the core competence
(the VRIN resources) of firms will not occur, thus it means no competitive advantages
emerge, and the adoption of tech-nology will
be less effective (Barney 1991; Priem and
Butler 2001). Consequently managers should
realize that technology’s adoption is not a
static process (Moore 1991; Rogers 1995). It
is not only about the relationship between
resources both inside and outside the organization, but also the ability of the organization to recognize the values of novelty in
the external forms, then assimilate and apply
them for commercial purposes, or the
company’s ability to evaluate and utilize external knowledge as the primary purpose of
the level of prior/previous knowledge (Link
and Siegel 2002; Zaheer and Bell 2005). Considering the externalities as an antecedent, to
achieve a successful tech-nology adoption,
managers must also acknowledge that the
influence of partners such as vendors, associations they belong to, R&D centers, universities, all commonly known as ‘network
effects’, are likely to significantly impact on
technology’s adoption since they can affect
the expected benefits from new technology
that exists with other assets of the firm (AlQirim 2006; Jeyaraj et al. 2006; Scupola
2009).
Examining further the three determinant factors of technology’s adoption, this
study found the technolog y adoption
organization’s typology consists of 8 (eight)
levels: ‘Ideal’ technology adoption with a high
absorptive capability (absorp), high entrepreneurial leadership (entrep) and high slack re-
Figure 2. Technology Adoption Organization in a 3D Matrix
250
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
sources (resou), ‘pro-active’ with high absorp,
high entrep and low resou, ‘bottom-up’ with high
absorp, low entrep and high resou, ‘organizational’ with high absorp, low entrep and low
resou, ‘top-down’ with low absorp, high entrep
and high resou, ‘entrepreneurial’ with low
absorp, high entrep and low resou, ‘slack’ with
low absorp, high entrep and low resou and ‘stagnant’ technology adoption with low absorp,
entrep and resou. In summary the technology
adoption organization can be plotted as in
Figure 2.
This study has examined the organization’s typology of technology’s adoption
related to the firms’ performance as in Table
5. It shows that the ‘ideal’ adoption has the
most significant effect on the firm’s performance (scoring 4.57 out of 6). On the other
hand the study found that most of the unit
analysis is pro-active organization (202 of 222
units) affecting the firm’s performance with
a score of 4.21 (lower than the ideal). This
finding supports the result of the empirical
research, proving that technology’s adoption
by PLN’s business units is significantly proven
to enhance the organization’s performance.
To achieve a robust hypothetical model,
this study has been limited and bounded by
several conditions. Firstly, the technology’s
adoption in this study is defined as ‘output;’
it is an outcome of the process of search and
selection; technology options are selected by
the organization; detailed understanding is
gained; and the new technology is used in new
products/services. This limited the scope of
the study, neglected other definitions, and put
technology’s adoption as just a ‘content’ of
the firm. However many studies argue that
technology’s adoption is mostly a ‘process’
in dynamic circumstances. Secondly, this study
is conducted at the intra-firm level (business
unit) of a utility industry; the findings might
not be transferable to other types of organizations. A highly regulated utility, such as the
electricity industry, is less influenced by the
market, so that an important externality such
as pressure from competitors is not applicable. Thirdly, this study relies on ‘snap shot’
data which do not provide any longitudinal
or time series data which examines the past,
present, and future of the relationships.
Table 5. The Typology of Technology Adoption Organization
Technology
Adoption
No. of
Unit
Absorp
Entrep
Resou
6
HIGH
HIGH
Pro-active
202
HIGH
Bottom-up
0
Organizational
Firm Performance
Scale
Score
HIGH
HIGH
4.57
HIGH
LOW
HIGH
4.21
HIGH
LOW
HIGH
LOW
-
10
HIGH
LOW
LOW
LOW
3.19
Top-down
0
LOW
HIGH
HIGH
LOW
-
Entrepreneurial
3
LOW
HIGH
LOW
LOW
3.38
Slack
1
LOW
LOW
HIGH
LOW
2.31
Stagnant
0
LOW
LOW
LOW
HIGH
-
Ideal
251
Arifin et al.
Therefore further research is highly recommended for ‘market-driven’ organizations
in dynamic industries with tough competition. It is also suggested that further research
can be conducted on multi-national organizations and also in countries with different
cultures for the external validity of the model.
In addition, further surveys can be designed
in such a way that firms’ performance,
technology’s adoption and leadership can be
measured by longitudinal data, to find their
consistency and to investigate further into the
process, and not only technology adoption’s
content.
Conclusion
The study has investigated the influence
of externalities and absorptive capability on
the adoption of technology, and others determinant factors for enhancing firms’ performance. Four determinant factors which
have been examined are: Externalities, slack
resources, entrepreneurial leadership and absorptive capability. The successful adoption
of technology by a firm can be only achieved
by an excellent absorptive capability with the
externalities as the antecedent. However the
effects of entrepreneurial leadership and
slack resources are not significant, even
though they are affected significantly by the
absorptive capability. The relationship between TOE’s components and absorptive
capability is positively significant. Meanwhile
technology’s adoption is proven to mediate
the absorptive capability with the performance of a firm.
252
Practically the model of this study is
mostly relevant for top corporate executives
(boards of directors) or top management
teams, who seek to provide some supporting
‘hardware’ content such as externalities, resources and leadership, and should improve
their firm’s ‘software’ abilities such as the absorptive capability, in order to achieve a successful technology adoption in their organization. Using the eight organizational typologies of technology’s adoption they will be
able to manage all the determinant factors
effectively, and achieve a successful adoption
in their organization. Without such a capability –at next higher order– for managing the
resource, the core competence (VRIN resources) of a firm will not occur, thus it means
no competitive advantages emerge. On the
other hand managers should utilize ‘vicarious learning’ or learning from the actions of
other firms (external) because adopting technology is a dynamic processes that can be
merged by inter-related organizational responses. Technology’s adoption depends on
prior knowledge and facilitates the cumulative learning of new related knowledge, efficient and effective coordination or integration of activities internal to the firm, as well
as the external coordination of activities and
technologies, via formal or informal cooperation between industries, university laboratories, and professional networks. Further research is recommended for different contexts
and to focus more on the process rather than
the content.
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
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Gadjah Mada International Journal of Business
Vol. 18, No. 3 (September-December 2016): 237-261
The Determinant Factors of Technology Adoption for
Improving Firm’s Performance:
An Empirical Research
of Indonesia’s Electricity Company
Zainal Arifin,1 Firmanzah,2 Avanti Fontana,2 and Setyo Hari Wijanto2
1)
PT PLN, Indonesia; 2) Faculty of Economics and Business, Universitas Indonesia
Abstract: This study investigates the determinant factors of technology adoption by connecting Technology Organizational Environment (TOE) with the dynamic capability factors. Using 518 respondents
representing 222 business units of Indonesia’s electricity company, the study found that only the absorptive capability has a positively significant effect on technology adoption. Practically, the study emphasizes
that without the absorptive capability for managing the resource, the core competence of a firm will not
occur and the adoption of technology will be less effective. Another finding is the absorptive capability’s
typology mapping the eight technology adoption statuses in an organization, based on three of the
determinant factors: the externalities, entrepreneurial leadership and slack resources.
Abstrak: Penelitian ini menguji berbagai faktor penentu pengadopsian teknologi dengan menghubungkan lingkungan
organisasional teknologi dengan faktor kapabilitas dinamis. Dengan menggunakan 518 responden yang mewakili 222 unit
bisnis Perusahaan Listrik Negara (PLN), studi ini menemukan bahwa hanya kapabilitas absorptif yang mempunyai
pengaruh positif signifikan pada pengadopsian teknologi. Dalam praktiknya, studi ini menekankan bahwa tanpa kapabilitas
absorptif untuk pengelolaan sumber daya, kompetensi inti perusahaan tidak akan tercipta, sehingga pengadopsian teknologi
menjadi kurang efektif. Temuan lain adalah tipologi kapabilitas absorptif memetakan 8 (delapan) status pengadopsian
teknologi dalam organisasi berdasarkan tiga faktor penentunya: eksternalitas, kepemimpinan kewirausahaan dan sumber
daya yang tertunda.
Keywords: absorptive capability; electricity utility; technology adoption; TOE framework
JEL classification: D83, L94, O33
* Corresponding author’s e-mail: zainal.arifin22@pln.co.id
ISSN: 1141-1128
http://journal.ugm.ac.id/gamaijb
237
Arifin et al.
Introduction
Technology has improved and continues to improve the way we live, communicate, interact socially and do business. In the
context of a firm, Dussauge (1992) argued
that technology is a factor affecting many
aspects of a firm’s strategy. The technological
changes and innovations were fundamental
sources of productivity and sustainable
growth (Morrris 1998; Johnson et al. 2008;
Van Ark et al. 2008). Thus, viewing technology’s adoption as a consistent process is
the key to successfully adopt and use technology. Strategically, the successful adoption
of technology by firms significantly affects
their competitive advantage, especially their
performance (Porter 1985; Barney 1991;
Majundar and Ventaraman 1998; Rayport and
Jaworski 2004; Kotler and Keller 2006).
Concerning this issue, some research
studying the use of technology in production
processes to increase a firm’s productivity has
been conducted in the 19th and 20th centuries (Abramovitz 1956; Solow 1957; Saloner
and Shepard 1995). Further studies have
linked technology to firms’ performance, as
measured through wages, the firms’ productivity, growth, and other factors (Bressler et
al. 2011). Many studies argued that technology’s adoption had significantly affected
the firms’ performance (SSinha and Noble
2008; Sabbaghi and Vaidyanathan 2008;
Benitez-Amado et al. 2010; Bressler et al.
2011; Adewoje and Akanbi 2012).
However the impact of technology’s
adoption remains inconclusive. Some studies proved that IT’s (Information Technology)
adoption contributed to an up to 81 percent
increase in output (Brynjolfsson and Hitt
2000), reduced labor costs by up to 40 percent (Rodd 2004), increased efficiency and
the total productivity of the adopting firms
238
(Chandrasekhar et al. 2008; Benitez-Amado
et al. 2010), enhanced the firms’ profitability
(Adewoje and Akanbi 2012; Kabiru and
Usman 2012), and improved the firms’ financial profits (Sarker and Valacich 2010). On
the other hand some empirical studies did not
find any relevant improvements in productivity associated with investment in technology (Quinn and Baily 1994; Becchetti et al.
2006). Berndt and Morrison (1995) also found
a negative relationship between profitability
and investment in IT. Thus, the notion of the
productivity paradox of IT was created and
has been one of the main issues in IT research
areas (Raymond and Blili 1997). Shu and
Strassmann (2005) also noticed that ICT
technology cannot improve firms’ earnings
in terms of their return on assets. In addition
a quantitative research by Jawabreh et al.
(2012) found that there was a negative correlation between the adoption of technology
and the profit rate of the airlines adopting it.
This paradox requires further research
to examine what are the initial determinant
factors of technology’s adoption and what is
the mediating factor which determines the
relationship between technology’s adoption
and firm’s performance. At the firms’ level,
the TOE (Technology - Organization - Environment) model has been considered to be
the most effective tool explaining technology’s
adoption (Oliveira and Martins 2011). However it is not sufficient to analyze technology’s
adoption in relation to a firm’s performance
in dynamic circumstances. Viewing technology’s adoption as a process for managing
some resources; it could be analyzed and determined by RBV (Resource Based View). In
order to develop its competitive advantage,
a firm must have resources and capabilities
that are superior to its competitors (Barney
1991). The firm’s resources and capabilities
together form its distinctive competencies.
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
Literature Review
The study of the adoption of technology can be approached from several levels
(Taylor and Tod 1995). Some researchers investigate its adoption from a macro-view
within the social context or at the country
level (Kiiski and Pohjola 2002). Others have
examined this issue at the organizational or
intra-firm level (Plouffe et al. 2001). Some
others focused on investigating technology’s
adoption by its individual determinants
(Bagozzi et al. 1992; Davis 1989; Venkatesh
et al. 2003).
Extending Taylor and Todd’s (1995)
classification, the research into the determinants of technology’s adoption could be distinguished into three streams: Firstly, those
based on intention-based models relying on
how users accept or do not accept it, and further use or reject technology; secondly, diffusion innovation, focusing on why and how a
new technology spreads around an organization or community; and thirdly, how the new
technology affects the goals, objectives and
performance of an organization. The first
stream was exemplified by such theories as
the Technology Acceptance Model or TAM
(Davis 1989) and the Unified Theory of Acceptance and Use of Technology (UTAUT)
(Venkatesh et al. 2003). The second stream
was primarily represented by the Diffusion
Of Innovation or DOI theory (Rogers 1963;
1983; 1995), and the Technology Adoption
Life Cycle or TALC model (Rogers et al. 1957;
Moore 1991). The last one was dominantly
explained by organizational theories such as
the Technology Organization and Environment or ‘TOE’ framework (Tornatzky and
Fleischer 1990). Considering the content and
context of the research (de Wit and Meyer
2010), and exploring all those main related
theories, this paper proposed the TOE frame-
work as the most relevant theory for searching for the determinant factors of technology’s adoption at the firms’ level. The
three elements present both constraints and
opportunities for technological innovation
(Oliveira and Martins 2011).
Although RBV could explain technology’s adoption processes for achieving a firm’s
competitive advantage, it is essentially a static
theory since it does not explain how the firm’s
resources and capabilities evolve over time
to be the basis of its competitive advantage
(Priem and Butler 2001). RBV research does
not essentially examine the effects of a firm’s
external environment on how it manages its
resources. Hence there was a need for a theory
which would not just view a firm as a bundle
of resources, but also the mechanisms by
which firms’ learn and accumulate new skills
and capabilities, and the forces that limit the
ratio and direction of this process (Teece et
al. 1990). Then the concept of ‘dynamic capability’ emerged; it reflects how quickly the
capabilities and resources of the company
change following changes in an increasingly
dynamic environment (Eisenhardt and Martin 2000).
Referring to the previous Dynamic Capabilities (DC) research, the most important
relationship in this field is that of dynamic
capabilities with performance. The literature
is divided (Silva 2007); some explain that
there is a direct relationship between firms’
dynamic capabilities and their performance
or competitive advantage (Makadok 2001;
Zollo and Winter 2002). Others have linked
dynamic capabilities to competitive advantage but have asserted that this link is indirect (Zott 2003; Helfat and Peteraf 2003;
Ambrosini and Bowman 2009; Wang and
Ahmed 2007). Contrary to those ideas,
Helfat and Peterraf (2003) argued that dynamic capabilities did not necessarily lead to
239
Arifin et al.
a competitive advantage. However to sustain
their competitive advantage, firms need to
renew their stock of valuable resources, as
their external environment changes because
of their dynamic capabilities’ processes
(Teece et al. 1997; Makadok 2001; Helfat
and Peterraf 2003; Wang and Ahmed 2007;
Majumdar et al. 2010).
Nowadays a firm’s absorptive capacity
is mostly conceptualized as a dynamic capability (Abreu et al. 2008). Following key empirical studies pertinent to DCs from 1996 to
2012, Dynamic capability in the form of absorptive capability has been implemented at
many levels and in the context of numerous
studies at this time. It has been widely examined at the firms’ level (Teece et al. 1997),
industries’ level (Lin and Lin 2008), intrafirm (Amlakuet al. 2012), inter-firm (Brady
and Davis 2004), SMEs (Griffith and Har vey
2001) and non-profit organizations (Zahra
and George 2002.
In addition absorptive capability is the
most applicable form of DC for many fields/
subjects. The implementation of absorptive
capability has been included in studies focusing on research and development (Caloghirou
et al. (2004), knowledge management (Corso
et al. 2006), organizational structures (Lin
2012), human resources (Caloghirou et al.
2004; Freels 2005), external interactions
(Caloghirou et al. 2004), social capital (Landry
et al. 2002), supplier integration (Malhotra
et al. 2005), client integration (Johnsen and
Ford 2006) and inter-organizational fit (Lane
and Lubatkin 1998).
These existing various relationships of
absorptive capability to firms’ performance
requires further research to examine what are
the determinant factors of absorptive capability’s effects and what is the mediating factor that connects the relationship between absorptive capability to firms’ performance.
240
Hypotheses
Following the TOE framework, there
were some relevant environmental factors
influencing firms’ adoption of technology,
such as the role of partners (Al-Qirim 2006;
Jeyaraj et al. 2006; Scupola 2009), competitive pressure (Porter and Millar 1985), and
regulatory compliance (Lai 2008, and Lin
2013). As well as their effect on the adoption of technology, those external factors also
influence how leaders’ perceive the way to
manage their organizations more efficiently
and effectively. External effects are mostly
related to the character and behavior of the
management, and how the organization interacts with its environmental dynamics; its
organizational leadership.
When the industrial environment was
more competitive, turbulent and unpredictable, it brought severe pressure to bear on
the types of analytical approaches to management. The cornerstone of competition
pushed people to think that analytical planning, which leads to competitive success, was
no longer feasible (Gupta et al. 2004). In this
chaotic and dynamic environment, where the
power of analytical leadership was diminished, the need the entrepreneurial leadership
by organizations was higher (McGrath 1997).
However the need for and emergence of entrepreneurial leadership was caused by the recent dynamic circumstance, so entrepreneurial leadership clearly was affected by external factors (McGrath and MacMillan 2000).
Additionally by considering the context of
our study, we prefer to use ‘externalities’ for
representing all the determinant factors of
technology’s adoption by an external organization; the external networks, regulations and
social issues etc.
Entrepreneurial leadership is known as
the dynamic process of presenting a vision,
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
building commitment among followers, and
risk acceptance when facing opportunities,
which causes the efficient use of the available resources, along with discovering and
utilizing new resources, with respect to the
leader’s vision (Lee and Venkataraman 2006).
The most important feature of entrepreneurial leadership is creating value, by discovering new opportunities and creating new strategies in order to gain a competitive advantage (Schulz and Hofer 1999). Moreover,
entrepreneurial leadership’s development occurs during the process of transforming the
knowledge acquired from experience and social interaction, allowing the opportunities for
personal development and business creation
to be identified (Churchill et al. 2013). Hence:
Hypothesis 1: the externalities positively affect the
entrepreneurial leadership.
On one hand, such a capability mostly
depends on technological collaborations, formal or informal networks between firms and
their external pressures, such as industries and
professional groups, and between industry
and university laboratories (Massini and
Lewin 2003). Furthermore, this relationship
would increase the effectiveness of knowledge’s absorption capability as it enhances the
complementary asset of experience inside the
firm (Cohen and Levinthal 1990). Then, collaboration and partnerships could be a learning resource for an organization, that helps
companies to recognize dysfunctional routines and to avoid hidden strategic constraints
(Teece 2007). While the study by Helfat and
Peterraf (2003) stated that all dynamic capability processes adapt, integrate and
reconfigure internal and external organizational skills, resources and functional
competences, in order to match the requirements of a changing environment. It was further emphasized by Rindova and Kotha
(2001) who explained how a dynamic market and turbulent industry pushes firms to
enter tough fields of competition through
their continuous organizational absorptive
capabilities. So:
Hypothesis 2: the externalities positively affect absorptive capability.
As well as its effect on absorptive capability, the externalities also had the effect
of slackening the resources. Bourgeois (1981)
argued that, in practical terms, slack resources
can serve some primary functions. They allow the organization to offer salaries that are
higher than those actually required to retain
the employees’ services. In addition slack resources also aid conflict resolution when
problem solving. Other functions of slack
resources are as a buffering mechanism, used
to adapt to sudden changes in the environment. Then the most important function of
slack resources is to facilitate strategic or creative behavior to help make long-term decisions such as seizing a business opportunity,
developing a new product, or realizing a
growth strategy. In summary, the functions
of slack resources are related to internal tensions within the organization, and also to external tensions between the organization and
its environment (the externalities). Then some
studies found that environmental conditions
or externalities are one of the antecedents
leading to the development of slack resources
(Stevens 2002; Donada and Dostaler 2005).
Thus:
Hypothesis 3: the externalities positively affect slack
resources.
Some recent literature has shown that
firms benefit from having absorptive capabilities when crafting new business and corporate strategies (Ambrosini and Bowman
2009); learning new skills (Zollo and Winter
2002; Ambrosini and Bowman 2009); lever241
Arifin et al.
aging their other resources (Ambrosini and
Bowman 2009); introducing innovative programs that stimulate strategic changes
(Repenning and Sterman 2002); and successfully commercializing new technologies generated through their R&D activities (Marsh
and Stock 2003). Other studies argue that absorptive capability’s effects are indirect; they
emerge through such things as entrepreneurial capability (Zahra 2006) and organizational
leadership (Hejazi et al. 2012).
Even though there are opposite views
in some researches’ findings about the importance of entrepreneurial and leadership activities for the conception, development, configuration and maintenance of the absorptive
capabilities in an organization (Repenning
and Sterman 2002; Zahra et al. 2006), some
recent studies argue that several best practice processes supporting knowledge acquisition, knowledge creation, and knowledge
integration in firms have affected their entrepreneurial leadership’s status (Simsek et al.
2010). So:
Hypothesis 4: the absorptive capability positively
affects entrepreneurial leadership.
Internally slack resources could be a
valuable resource to improve firms’ performance through managerial initiatives. Indeed,
managerial capability is necessary to find
ways to devote slack resources to productive
activities (Burkart et al. 2003). A positive relationship between slack resources and firms’
performance is more likely in firms with
higher levels of managerial dynamic capability (McKelvie and Davidsson 2009). They
found that four dynamic capabilities had positive effects stemming from their access to
particular resources, and provided empirical
support for the notion of treating the firm as
a dynamic flow of resources, as opposed to a
static stock. Along with the study, recent
242
empirical scholarship suggests that in both
the dynamic managerial capabilities (Ireland
et al. 2003) and the micro-level foundations
of routines and capabilities (Felin and Foss
2005), researchers have found that firms’
absorptive capabilities have fundamental roles
in the fir ms’ slack resources’ status
(McKelvie and Davidsson 2009). Hence:
Hypothesis 5: the absorptive capability positively
affects slack resources.
Due to investigating technology’s adoption using RBV logic, which leads to static
circumstance, firms need dynamic capabilities to renew their stocks of valuable resources, as their external environment
changes (Teece et al. 1997; Makadok 2001;
Wang and Ahmed 2007; Majumdar et al.
2010). Considering that a firm’s absorptive
capacity is mostly conceptualized as a dynamic
capability, which has been widely researched
at the level of firms, sectors, regions and nations, based on a wide consensus (Abreu et
al. 2008). It is the ability of the organization
to recognize the values of novelty in its external forms, then assimilate and apply it for
commercial purposes (Cohen and Levinthal
1990). Specifically, the absorptive capability
measures a firm’s ability to absorb, assimilate, and exploit an innovation throughout the
firm (Link et al. 2002). The higher the level
of its absorptive capability a firm demonstrates, the more it exhibits its dynamic capabilities (Zaheer and Bell 2005).
Regardless that there are different views
of dynamic capabilities’ effects on firms’ advantages, recently there have emerged studies arguing that the absorptive capability
builds and reconfigures resource positions
(Eisenhardt and Martin 2000), zero-order
capabilities (Winter 2003), operational routines (Zollo and Winter 2002) or operational
capabilities (Helfat and Peteraf 2003) and,
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
through them, affects performance. This
chain of causality is designated as an indirect link between absorptive capabilities and
performance. The indirect relationship results
from the idea that absorptive capabilities originated and defined firms’ individual resource
configurations, including their functional capability processes, which shape the firms’
competitiveness and therefore performance
(Zott 2003). Therefore this paper argues that
technology’s adoption is one of the functional
capabilities which mediate the relationship
between the absorptive capabilities and the
firm’s performance. Thus:
Hypothesis 6: the absorptive capability positively
affects technology’s adoption.
Based on strategic management practices, another significant organizational factor of technology’s adoption is organization
leadership. Fundamentally, leadership has an
influence over organizations via their strategic decision-making, determining organizational structures and managing the organizational process (Day and Lord 1988;
Nahavandi 1993). Moreover a leader with
good perceptional resources will contribute
to higher performance (Dessler 1994). Additionally Devarajan et al. (2003) found that
the success of firms in dynamic industries
depended on ‘thriving innovation activity’,
that in turn is primarily driven by ‘effective
entrepreneurial leadership’. Such leadership,
as represented by top management, plays a
very critical role in driving innovation in firms,
and in mastering its dynamics (Kuczmarski
1998; Kipp and Michael 2001). It could be
summarized that the key factor determining
successful technological adoptions under
these circumstances is the ‘effective entrepreneurial leadership’ of the organization
(Devarajan et al. 2003). So:
Hypothesis 7: the entrepreneurial leadership positively affects technology’s adoption.
TOE’s technological and organizational
context describes that technology’s adoption
depends on the pool of resources exceeding
the minimum necessary to produce a given
level of organizational output, or slack resources (Lin 2013). The raw materials’ input
to technology’s adoption process also includes tangible and intangible assets (Prakash
et al. 2008). Instead of technology, some
TOE focused studies postulated that slack
sources for technology’s adoption were financial (Franquesa and Brandyberry 2009),
knowledge (Jeyaraj et al. 2006; Sabherwal et
al. 2006; Lin 2013; Wang et al. 2013), and
employee or human capital (Wang et al. 2013;
Vanacker et al. 2013). In conclusion, such
resources have a positive effect on a firm’s
flexibility and innovation in a dynamic environment, (Damanpour 1996; Judge et al.
2001) and provide organizations with the
ability to be proactive as well as defensive in
adopting new technologies or designing new
lines of services (Lawson 2001; and Daniels
et al. 2004). Thus:
Hypothesis 8: slack resources positively affect the
adoption of technology.
Although there are still debatable results
about the effect of technology’s adoption on
firms’ performance; most literature shows
that the use of new technology during production increases a fir m’s productivity
(Abramovitz 1956; Solow 1957; Saloner and
Shepard 1995). Many recent researchers argue that technology’s adoption brings down
the operational costs (Saloner and Shepard
1995; Rodd 2004; Chandrasekhar et al. 2008;
Benitez-Amado et al. 2010), contributes to
output increases, even if they are only marginal, (Brynjolfsson and Hitt 2000; and
243
Arifin et al.
Adewoje et al. 2012), improves efficiency and
effectiveness (Milne 2006; Sabbaghi and
Vaidyanathan 2008; Rusli 2012), reduces
environmental impacts by lowering energy
costs (Bressler et al.; 2011), and also leads to
significant reductions in firms’ mortality rates
(Sinha and Noble 2008). Matching those studies, we argue that technology’s adoption has
significant effects on a firm’s performance.
Hence:
ship, slack resources and technology’s adoption. While both entrepreneurial leadership
and slack resources directly affect technology’s adoption, eventually technology’s
adoption will affect firms’ performance. However the model should be tested empirically
through a quantitative approach.
Hypothesis 9: the adoption of technology positively
affects the firm’s performance
Research Objective
The Proposed Model
All nine hypotheses construct a hypothetical conceptual model as the following
figure (Figure 1) shows, explaining the relationship between the determinant factors of
technology’s adoption at the firms’ level. The
model’s logic starts from externalities as the
antecedents; the organization is driven by the
external factors. It has an effect on the three
organizational factors: Entrepreneurial leadership, absorptive capability, and slack resources. So absorptive capability is the determining factor for entrepreneurial leader-
Methods
Starting from the TOE’s view, this study
analyzes the influence of externalities and absorptive capability on technology’s adoption
for improving firms’ performance. Other determinant factors –entrepreneurial leadership and slack resources– were also investigated to assess the relationship between
TOE’s factors with the absorptive capability
at the firms’ level. The study purposes to
empirically test a conceptual model of the
indirect effects of the externalities and absorptive capability on the adoption of technology, which can be the key predictors of
firms’ performance in a dynamic environment.
Figure 1. The Conceptual Hypotheses Model
Enterpr
H1
H4
H5
H3
Slack
244
H6
H8
Techn
H9
Extern
Absorb
H2
H7
Firm
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
Respondents and Procedure
Using an online survey, this study was
conducted by collecting data from 518 managers representing 229 business units around
Indonesia belonging to the Indonesian Electricity Company (PLN). There were 222 units
(96.94%) that completed the survey correctly,
1 unit’s reply (0.4%) was considered not to
be valid, and 6 units (2.6%) did not respond
at all. PLN was chosen because it represents
the content and context of the research issues, namely: (1) A ‘RBV perspective’ company (2) a ‘technology-intensive’ organization
where 87 percent of its assets are technological things (3) a ‘comprehensive’ technology
adoption flow, which covers both ‘top-down’
and ‘bottom-up’ processes (4) it is a ‘national
company’ with 40,000 employees spread out
over all areas of the country.
The respondents’ core business unit is
for the organization of the electricity utility
company’s supply chain, for the generation,
transmission and distribution or retail/service of electricity. Considering their similarity characteristics, the working experience of
the respondents is varied, from less than 5
years, while others have 5-10 years experience and over. Meanwhile their ages are divided into three groups: Less than 30 years
old, between 30-40 years old and over 40
years old. The number of samples, which
exceeds 100 questionnaires, is an appropriate number for research that analyzes its data
using a SEM (Structural Equation Model),
especially for the overall fit measures side,
which is represented by the likelihood-ratio
chi-square statistic (Hair et al. 1998). The
SEM analysis, which uses LISREL version
8.7 software, is done with a ‘two-stage approach,’ (Wijanto 2008), with the process as
follows: (1) Analysis of the measurement
model using Confirmatory Factor Analysis
(CFA). (2) Analysis of the structural model
to analyze the relationship among all the latent variables that have been simplified. (3)
Analysis of the significance test results for
each hypothesis to determine whether the
hypothesis will be accepted or rejected.
Measurement
For measuring the externalities, 9 selected items are used, with reference to a previous study with 3 controlling variables: An
external network ( Lin and Lin 2008), regulation (Lai 2008), and social issues (Asres et
al. 2012). Meanwhile entrepreneurial leadership is measured by the construct of the entrepreneurial leadership data from the
GLOBE (Global Leadership and Organizational Behavior Effectiveness) program
(Gupta et al. 2004). It consists of 2 main dimensions: Cast enactment and transformational enactment with a total of 19 items.
Then DC is represented by ‘absorptive capability’, which is summarized from previous
DC literatures (Abreu et al. 2008). It has 4
dimensions: Knowledge acquisition, assimilation, transformation and exploitation, with
12 items. Slack resources are represented by
3 variables: technology, knowledge, and human resources (Wang et al. 2013) with 10
items. Regarding the context of this study,
slack finance is neglected. Technology’s adoption, which has 6 items, is measured through
2 dimensions: appropriateness and effectiveness (Mirvis et al. 1991; Hall and Kahn
2002). Then the firm’s performance is observed by financial and non-financial variables with 6 items (Kabiru and Usman 2012).
Overall, 62 items using 6 Likert-type scales
are used to measure the 6 latent variables.
245
Arifin et al.
Result
lower than their standard (RSMEA and Std
RMR). Using LISREL the test presents the
calculated value of RSMEA = 0.044, NFI =
0.97, NNFI = 0.98, CFI = 0.99, IFI = 0.99,
RFI = 0.96, Std RMR = 0.045, GFI = 0.91
and AGFI = 0.87. Table 2 shows that there
is only one GOFI indicator that shows a marginal fit (AGFI), therefore it can be concluded that the overall fit of the structural
model is good.
In addition CFA also measures the validity for all the indicators (observed variables) and the reliability of the measurement
for each latent variable (construct). As presented in Table 3, from the 19 observed variables of the model, there are 2 indicators of
the externalities construct (regulation and
social issues) which have a SLF (Standardized Loading Factor) of < 0.70 (not good
validity). The table also shows that 2 of the
6 latent variables have a CR (Construct Reliability) score of < 0.70 and a VE (Variance
Extracted) score of < 0.50. This means that
the latent variables of the externalities and
slack resources are less reliable. However the
CFA test confirmed that the overall variables
of the measurement model have good reliability and validity.
The loading factors for all the items can
be seen in Appendix 1, and the result of the
descriptive statistical analysis can be seen in
Table 1. Even though the standard deviation
is included in the analysis, the latent variables’
score is still higher than average. For example,
slack resources, with a standard deviation of
0.53, has a lowest limit of 2.66 (3.19 – 0.53).
This value shows that most of the respondents believe that their actual perceptions are
similar. Additionally based on the ANOVA
test result, in general there are no differences
for all the latent variables in the respondent’s
profile group which refer to those discriminate factors: Type, location and the size of
the organization’s respondents.
Then a Confirmatory Factor Analysis
(CFA) is used to test how well the measured
variables represent the number of constructs.
It is conducted to specify the number of factors required in the data, and which measured
variable is related to which latent variable. To
have a good overall fit of the measurement
model, some of the GOFI (Goodness of Fit
Index) indicators should be higher than the
standard values (NFI, NNFI, CFI, IFI, RFI,
GFI and AGFI) and two others should be
Table 1. Descriptive Statistics Analysis Result
246
Construct
Number
of items
Mean
Standard
Deviation
Skewness
Kurtosis
Externalities
Entrepreneurial leadership
Absorptive capability
Slack resources
Technology adoption
Firm performance
9
19
12
10
6
6
4.77
4.96
4.53
2.81
4.76
4.14
0.54
0.59
0.57
0.53
0.59
0.68
-0.67
-0.94
-0.43
-0.36
-0.94
-0.75
1.20
1.66
1.05
0.83
2.90
1.57
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
Table 2. GOFI Values of the Structural Model Test
GOFI
RSMEA
NFI
NNFI
CFI
IFI
RFI
Std. RMR
GFI
AGFI
Calculated Values
Standard Value
0.04
0.97
0.99
0.99
0.99
0.96
0.048
0.94
0.87
<
>
>
>
>
>
<
>
>
Conclusion
Fit is good
Fit is good
Fit is good
Fit is good
Fit is good
Fit is good
Fit is good
Fit is good
Fit is marginal
0.08
0.90
0.90
0.90
0.90
0.90
0.05
0.90
0.90
Table 3. Validity and Reliability of Measurement CFA Model
Variables/
Dimensions
*SLF ≥0.5
Absorptive capability
AcquL
0.86
AssiL
0.94
TranL
0.90
ExploL
0.78
Adoption Technology
ApproL
1.00
EffectL
0.81
Entrepreneurial leadership
BuildL
0.84
DefL
0.85
ChalL
0.94
AbsorL
1.00
UnderL
0.92
Externalities
NetL
0.78
RegL
0.49
SocL
0.45
Firm performance
FinL
0.70
NonfinL
0.92
Resource slack
TechL
0.62
KnowL
0.96
HumL
0.94
Error
*CR ≥0.7 *VE ≥0.5
0.27
0.12
0.20
0.39
0.01
0.35
0.29
0.28
0.11
0.01
0.15
0.39
0.75
0.80
0.52
0.14
0.63
0.10
0.19
0.76
0.77
0.84
0.90
0.94
0.81
0.54
0.65
0.83
0.51
0.49
0.49
*)
SLF = Standardized Loading Factor; where a good SLF score is > 0.50
**)
CR = Construct Reliability; where a good CR score is > 0.70
***)
VE = Variance Extracted; where a good VE score is > 0.50
Conclusion
Good reliability
Good validity
Good validity
Good validity
Good validity
Good reliability
Good validity
Good validity
Good reliability
Good validity
Good validity
Good validity
Good validity
Good validity
Less reliable
Good validity
Marginal validity
Marginal validity
Good reliability
Good validity
Good validity
Less reliable
Good validity
Good validity
Good validity
247
Arifin et al.
Table 4. Test Results of the Structural Research Model
Hypotheses
H1
Latent Variable’s
Relationship
Externalities
Entrepreneurial
leadership
Calculated
t-value
Structural
Coefficient
0.23
0.04
There is an insignificant
positive effect, hypothesis 1 is
rejected.
H2
Externalities
Absorptive capability
10.61
0.85
There is a significant positive
effect, hypothesis 2 is accepted.
H3
Externalities Slack
resources
0.03
0.01
H4
Absorptive capability
Entrepreneurial
leadership
There is an insignificant
positive effect, hypothesis 3 is
rejected.
3.62
0.65
There is a significant
positive effect, hypothesis 4
is accepted.
H5
Absorptive capability
Slack resources
2.66
0.53
H6
Absorptive capability
Technology adoption
There is a significant
positive effect, hypothesis 5 is
accepted
5.58
0.68
There is a significant
positive effect, hypothesis 6 is
accepted
H7
Entrepreneurial leadership
Technology adoption
-0.81
-0.07
There is an insignificant
negative effect, hypothesis 7 is
rejected
H8
Slack resources
Technology adoption
-0.62
-0.05
H9
Technology adoption
Firm performance
There is an insignificant
negative effect, hypothesis 8 is
rejected
5.23
0.84
There is a significant
positive effect, hypothesis 9 is
accepted
The calculated t-values and structural
coefficients of each latent variable, and the
hypotheses results are presented in Table 4.
From the hypotheses testing results, five hypotheses are accepted (t-value > 1.96) and four
hypotheses are rejected (t-value < 1.96). It is
surprising, finding that the externalities have
no significant effect on both entrepreneurial
leadership and slack resources (H1, H3). In
conclusion the model demonstrates that the
determinant factors of technology’s adoption
248
Conclusion
for improving a firm’s performance works
only through 1 pathway: Externalities –absorptive capability– technology adoption
(H2, H6). Even though the absorptive capability has positive significant effects on both
entrepreneurial leadership and slack resources
(H4, H5), the relationship does not significantly affect the adoption of technology
(both H7 and H8 are rejected). The study has
empirically proven that the effect of dynamic
capability –in this study represented by ab-
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
sorptive capability– on firms’ performance is
indirect mediated by technology’s adoption
(H6, H9).
Discussion
Starting from the TOE’s framework, this
study is focusing on the determinant factors
of technology’s adoption at the firms’ level,
and especially the role of externalities as the
primary antecedent and absorptive capability as a dynamic factor. Through an empirical
conceptual model, this study has found and
tested the cor relation of technology
adoption’s antecedents –in the form of the
TOE’s factors– (H1, H3), and then connected
them to a firm’s DCs (H2, H4, H5) and eventually to a firm’s performance through
technology’s adoption (H6, H7, H8, H9).
Many studies using the TOE framework have
proven that the determinants’ factors have a
significant relationship with technology’s
adoption for enhancing firms’ performance
(Al-Qirim 2006; Jeyaraj et al. 2006; Scupola
2009; Lai 2008; Lin 2013).
However, this study empirically proved
that entrepreneurial leadership has an insignificant negative relationship with technology’s adoption (H7); as well as slack resources (H8). Based on further investigation
most of the process of technology’s adoption by PLN’s business units is ‘bottom-up;’
it is likely driven by the organization rather
than the management. This is why entrepreneurial leadership has a negative insignificant
effect on technology’s adoption (Greenberger
and Sexton 1988; Roomi and Harrison 2011).
The slack resources also have a negative insignificant effect, due to some important resources such as financial and human capital,
which are not controllable by the business
units’ managers (Chau and Hui 2001 and
Franquesa and Brandyberry 2009). Those re-
sources are mostly managed by PLN’s regional
offices so that resources are ‘given’ to the
business units (Vanacker et al. 2013).
In addition, previous literature shows
that the relationship between TOE’s factors
theoretically is significantly positive, such as
the externalities to entrepreneurial leadership
(McGrath and MacMillan 2000; Cohen and
Levinthal 1990) and externalities to slack resources (Pfeffer and Salancik 1978; Sharfman
et al. 1988; Stevens 2002; Donada and
Dostaler 2005). However this study shows
that the externalities affect both entrepreneurial leadership and slack resources positively,
but not significantly (H1, H3). The insignificant effect of the externalities on entrepreneurial leadership is caused by the relationship between PLN’s business units and their
networks, such as government agencies, the
industry and their professional associations,
none of which are relevant to the development of entrepreneurial and leadership capabilities in the organization. A similar reason is also applicable for the positive, insignificant effect of the externalities on the slack
resources in PLN’s business units (Iacovau
et al. 1995, and Lin 2013).
With the recent turbulent and unpredictable circumstances there is a need for directly
connecting the TOE’s factors to the absorptive capability, especially to show the adoption of technology as a functional competence/capability in a dynamic business environment (H2, H4, H5). Moreover the study
empirically proves that the effect of absorptive capability –as a representative of DC on the firm’s performance is indirect and - in
this research– mediated by technology’s adoption (H6, H9). This supports some previous
studies (Zott 2003; Helfat and Peteraf 2003;
Ambrosini and Bowman 2009; Wang and
Ahmed 2007). Practically, the adoption of
technology by PLN’s business units is mostly
249
Arifin et al.
an output of such absorptive capability, such
as knowledge management practices.
Technology’s adoption is commercial ‘knowledge implementation;’ it is one of the stages
of the knowledge management cycle in an
organization. Therefore like other intangible
resources; its effect on the organization’s performance takes a relatively long time to be
felt, as it is not direct.
This study emphasizes that, without absorptive capability –at next higher order– for
managing the resource, the core competence
(the VRIN resources) of firms will not occur, thus it means no competitive advantages
emerge, and the adoption of tech-nology will
be less effective (Barney 1991; Priem and
Butler 2001). Consequently managers should
realize that technology’s adoption is not a
static process (Moore 1991; Rogers 1995). It
is not only about the relationship between
resources both inside and outside the organization, but also the ability of the organization to recognize the values of novelty in
the external forms, then assimilate and apply
them for commercial purposes, or the
company’s ability to evaluate and utilize external knowledge as the primary purpose of
the level of prior/previous knowledge (Link
and Siegel 2002; Zaheer and Bell 2005). Considering the externalities as an antecedent, to
achieve a successful tech-nology adoption,
managers must also acknowledge that the
influence of partners such as vendors, associations they belong to, R&D centers, universities, all commonly known as ‘network
effects’, are likely to significantly impact on
technology’s adoption since they can affect
the expected benefits from new technology
that exists with other assets of the firm (AlQirim 2006; Jeyaraj et al. 2006; Scupola
2009).
Examining further the three determinant factors of technology’s adoption, this
study found the technolog y adoption
organization’s typology consists of 8 (eight)
levels: ‘Ideal’ technology adoption with a high
absorptive capability (absorp), high entrepreneurial leadership (entrep) and high slack re-
Figure 2. Technology Adoption Organization in a 3D Matrix
250
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
sources (resou), ‘pro-active’ with high absorp,
high entrep and low resou, ‘bottom-up’ with high
absorp, low entrep and high resou, ‘organizational’ with high absorp, low entrep and low
resou, ‘top-down’ with low absorp, high entrep
and high resou, ‘entrepreneurial’ with low
absorp, high entrep and low resou, ‘slack’ with
low absorp, high entrep and low resou and ‘stagnant’ technology adoption with low absorp,
entrep and resou. In summary the technology
adoption organization can be plotted as in
Figure 2.
This study has examined the organization’s typology of technology’s adoption
related to the firms’ performance as in Table
5. It shows that the ‘ideal’ adoption has the
most significant effect on the firm’s performance (scoring 4.57 out of 6). On the other
hand the study found that most of the unit
analysis is pro-active organization (202 of 222
units) affecting the firm’s performance with
a score of 4.21 (lower than the ideal). This
finding supports the result of the empirical
research, proving that technology’s adoption
by PLN’s business units is significantly proven
to enhance the organization’s performance.
To achieve a robust hypothetical model,
this study has been limited and bounded by
several conditions. Firstly, the technology’s
adoption in this study is defined as ‘output;’
it is an outcome of the process of search and
selection; technology options are selected by
the organization; detailed understanding is
gained; and the new technology is used in new
products/services. This limited the scope of
the study, neglected other definitions, and put
technology’s adoption as just a ‘content’ of
the firm. However many studies argue that
technology’s adoption is mostly a ‘process’
in dynamic circumstances. Secondly, this study
is conducted at the intra-firm level (business
unit) of a utility industry; the findings might
not be transferable to other types of organizations. A highly regulated utility, such as the
electricity industry, is less influenced by the
market, so that an important externality such
as pressure from competitors is not applicable. Thirdly, this study relies on ‘snap shot’
data which do not provide any longitudinal
or time series data which examines the past,
present, and future of the relationships.
Table 5. The Typology of Technology Adoption Organization
Technology
Adoption
No. of
Unit
Absorp
Entrep
Resou
6
HIGH
HIGH
Pro-active
202
HIGH
Bottom-up
0
Organizational
Firm Performance
Scale
Score
HIGH
HIGH
4.57
HIGH
LOW
HIGH
4.21
HIGH
LOW
HIGH
LOW
-
10
HIGH
LOW
LOW
LOW
3.19
Top-down
0
LOW
HIGH
HIGH
LOW
-
Entrepreneurial
3
LOW
HIGH
LOW
LOW
3.38
Slack
1
LOW
LOW
HIGH
LOW
2.31
Stagnant
0
LOW
LOW
LOW
HIGH
-
Ideal
251
Arifin et al.
Therefore further research is highly recommended for ‘market-driven’ organizations
in dynamic industries with tough competition. It is also suggested that further research
can be conducted on multi-national organizations and also in countries with different
cultures for the external validity of the model.
In addition, further surveys can be designed
in such a way that firms’ performance,
technology’s adoption and leadership can be
measured by longitudinal data, to find their
consistency and to investigate further into the
process, and not only technology adoption’s
content.
Conclusion
The study has investigated the influence
of externalities and absorptive capability on
the adoption of technology, and others determinant factors for enhancing firms’ performance. Four determinant factors which
have been examined are: Externalities, slack
resources, entrepreneurial leadership and absorptive capability. The successful adoption
of technology by a firm can be only achieved
by an excellent absorptive capability with the
externalities as the antecedent. However the
effects of entrepreneurial leadership and
slack resources are not significant, even
though they are affected significantly by the
absorptive capability. The relationship between TOE’s components and absorptive
capability is positively significant. Meanwhile
technology’s adoption is proven to mediate
the absorptive capability with the performance of a firm.
252
Practically the model of this study is
mostly relevant for top corporate executives
(boards of directors) or top management
teams, who seek to provide some supporting
‘hardware’ content such as externalities, resources and leadership, and should improve
their firm’s ‘software’ abilities such as the absorptive capability, in order to achieve a successful technology adoption in their organization. Using the eight organizational typologies of technology’s adoption they will be
able to manage all the determinant factors
effectively, and achieve a successful adoption
in their organization. Without such a capability –at next higher order– for managing the
resource, the core competence (VRIN resources) of a firm will not occur, thus it means
no competitive advantages emerge. On the
other hand managers should utilize ‘vicarious learning’ or learning from the actions of
other firms (external) because adopting technology is a dynamic processes that can be
merged by inter-related organizational responses. Technology’s adoption depends on
prior knowledge and facilitates the cumulative learning of new related knowledge, efficient and effective coordination or integration of activities internal to the firm, as well
as the external coordination of activities and
technologies, via formal or informal cooperation between industries, university laboratories, and professional networks. Further research is recommended for different contexts
and to focus more on the process rather than
the content.
Gadjah Mada International Journal of Business – September-December, Vol. 18, No. 3, 2016
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